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Knowledge-based fuzzy neural networks

  • Communications Session 4a Approximate Resoning
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Foundations of Intelligent Systems (ISMIS 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1079))

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Abstract

Knowledge-based neural networks are concerned with the use of numerical information, which forms the domain knowledge, obtained from sensor measurements to determine the initial structure of a neural network. Research on combining symbolic inductive learning with neural networks, as well as research on combining fuzzy logic with neural networks, is proceeding on several fronts. Fuzzy decision trees and their various algorithmic implementations are one of the most popular choices in applications to learning and reasoning from feature-based examples. Such constructions have drawn increasing attention recently due to comprehensibility of the generated knowledge structure, and wide availability of data in the form of feature descriptions. However, the inability of coping with missing data, imprecise or vague information, and measurements errors create a lot of problems for symbolic artificial intelligence. These problems might be overcome by employing fuzzy methodology. In this paper we present an approach based on fuzzy neural trees for determining the structure of a neural network. An analysis of digital thallium-201 myocardial scintigraphs is presented to corroborate the theory and demonstrate the utility of the approach.

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Authors and Affiliations

  1. Computer Science Department, Philadelphia College of Textiles and Science, 19144, Philadelphia, PA, USA

    Les M. Sztandera

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  1. Les M. Sztandera
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Zbigniew W. Raś Maciek Michalewicz

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© 1996 Springer-Verlag Berlin Heidelberg

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Sztandera, L.M. (1996). Knowledge-based fuzzy neural networks. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_159

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  • DOI: https://doi.org/10.1007/3-540-61286-6_159

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61286-5

  • Online ISBN: 978-3-540-68440-4

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